Companies Cash App

Cash App

Kitchener, Ontario, CAN

Total Offices: 2

3,500 Total Employees

Year Founded: 2013

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We're Hiring!

AI & Machine Learning 13 Product Management 6 Data & Analytics 5 Program & Project Management 5 Engineering 4 Marketing 3 Sales 3 Design 2 Operations & Support 1 Research 1

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Offices at Cash App

Hybrid Workplace

Employees engage in a combination of remote and on-site work.

Cash App has always been a distributed team working across timezones and continents. Today, we're remote-first by default, but encourage you to choose how you want to work: from home, one of our offices, or a mix of both.

Typical time on-site: Flexible

Global Office Locations

Kitchener, Ontario, CAN Toronto, Ontario, CAN

Where engineers roam free. One of the world's foremost engineering hubs. Kitchener-Waterloo is home to our very first Engineering team. Also skating, curling, and some notorious barbecues.

The Big Smoke. The Six. Home of Drake. The Toronto space is a hub for Machine Learning and Engineering, a sponsor of community events, and a community of plant lovers.

Recently posted jobs

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Cash App

Senior Data Scientist, Identity

4 Hours AgoSaved

Remote or Hybrid

Blockchain • Fintech • Mobile • Payments • Software • Financial Services

Lead identity-focused data science efforts: analyze large datasets, build statistical and experimental analyses, design/measure A/B tests, create ETL and forecasts, partner with product, engineering, risk, and compliance to maintain high-quality identity data and inform policy/decisioning.

Cash App

Solutions Engineer

4 Hours AgoSaved

Remote or Hybrid

Blockchain • Fintech • Mobile • Payments • Software • Financial Services

Lead the implementation of enterprise integrations for Block, managing projects and stakeholder relationships to ensure successful delivery within timelines.

Cash App

Senior Machine Learning Engineer, Risk Signals & Controls

YesterdaySaved

Remote or Hybrid

Blockchain • Fintech • Mobile • Payments • Software • Financial Services

Design and maintain ML pipelines and data pipelines, optimize fraud detection models, collaborate with teams, and ensure data quality for high-scale applications.

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